Habitat productivity constrains the distribution of social spiders across continents – case study of the genus Stegodyphus

Table 1 Determinants that a given Stegodyphus occurrence record belongs to a social species, assessed by logistic regression modelling with information-theoretic model selection

Model/predictors						K	∆ AIC	w_i	TSS	R²
Region			GVI		SF	9	0.321	0.271	0.303	0.425
Region			GVI	*I (Region GVI)**	SF	10	2.069	0.113	0.318	0.425
Region	PSea		GVI		SF	10	1.965	0.119	0.308	0.425
Region	PSea	I (Region* PSea)	GVI	I (Region* GVI)	SF	12	5.260	0.023	0.324	0.427
Region	PSea				SF	9	20.886	0.000	0.328	0.371
Region	PSea	I (Region* PSea)			SF	10	22.727	0.000	0.306	0.372
	PSea		GVI		SF	8	1.406	0.157	0.305	0.417
			GVI		SF	7	0.000	0.318	0.306	0.415
	PSea				SF	7	18.428	0.000	0.342	0.368
					SF	6	19.752	0.000	0.355	0.359

Abbreviations of the predictors used in each of the 10 models for logit link, are as follows (without the intercept): Region for regional variable (combination of 2 binaries); GVI for vegetation productivity; PSea for precipitation seasonality; I (Region* GVI and Region*PSea) for interaction terms; and SF for six spatial filters used. Best supported models are shown in bold. K is the number of model parameters, ∆ AIC are AIC differences, wi Akaike weights of each model; and TSS is the true skill statistics score of each model (see Methods section for more explanation on the last three).

ISSN: 1742-9994